Особине личности и анксиозност у вези са вештачком интелигенцијом код просветних радника у Србији: посредничка улога отпорности, радног локуса контроле и синдрома сагоревања на раду

Ključne reči: особине личности, отпорност, радни локус контроле, синдром сагоревања на раду, вештачка интелигенција, анксиозност, просветни радници

Sažetak


У контексту убрзаног технолошког напретка, посебно у области образовања, све израженија примена вештачке интелигенције отвара питање начина на који индивидуалне психолошке карактеристике утичу на доживљавање и регулацију анксиозности у вези са овим технолошким променама код наставног особља. Циљ овог истраживања био је да се испита да ли отпорност, радни локус контроле и синдром сагоревања на раду посредују у односу између особина личности и нивоа анксиозности у вези са вештачком интелигенцијом код просветних радника у Републици Србији. Истраживање је спроведено на узорку од 324 наставника и професора из основних и средњих школа. У истраживању су коришћени следећи инструменти: Десетоајтемски инвентар личности (TIPI–10) за процену димензија личности, Кратка скала резилијентности (BRS) за мерење отпорности, Спекторова скала радног локуса контроле (SWLC), Копенхашки инвентар сагоревања (CBI) за процену синдрома сагоревања и Скала анксиозности у вези са вештачком интелигенцијом (AIA). Резултати показују да особине личности имају ограничен директан утицај на анксиозност у вези са вештачком интелигенцијом. Неуротицизам доприноси вишим нивоима анксиозности индиректно, путем спољашњег радног локуса контроле и синдрома сагоревања на раду. Насупрот томе, савесност и отпорност делују заштитно, јер умањују синдром сагоревања и јачају унутрашњи радни локус контроле, који се показују као значајни предиктори ниже анксиозности у вези са вештачком интелигенцијом. Ови налази указују на значај јачања унутрашњих психолошких капацитета просветних радника у процесу прилагођавања условима образовног система који се непрестано и често непредвидиво мења.

Reference

Alarcon, G., Eschleman, K. J., & Bowling, N. A. (2009). Relationships between personality variables and burnout: A meta-analysis. Work & Stress, 23(3), 244–263. https://doi.org/10.1080/02678370903282600

Angelini, G. (2023). Big Five model personality traits and job burnout: A systematic literature review. BMC Psychology, 11, 49. https://doi.org/10.1186/s40359-023-01056-y

Azarkerdar, F., Pourehsan, S., & Towhidi, A. (2022). The mediating role of resilience in the relationship between personality traits with job satisfaction in teachers. Educational Psychology, 18(65), 183–201. https://doi.org/10.22054/jep.2022.63789.3482

Babiker, A., Alshakhsi, S., Al-Thani, D., Montag, C., & Ali, R. (2024). Attitude towards AI: Potential influence of conspiracy belief, XAI experience and locus of control. International Journal of Human–Computer Interaction, 41(13), 7939–7951. https://doi.org/10.1080/10447318.2024.2401249

Babiker, M., Merisalu, E., Roja, Ž., & Kalkis, H. (2025). Prospective effects of artificial intelligence on burnout syndrome: Reducing risks and enhancing psychological well-being. Sigurnost: časopis za sigurnost u radnoj i životnoj okolini, 67(2), 135–141. https://doi.org/10.31306/s.67.2.4

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality & Social Psychology, 51, 1173–1182.

Basha, S. E., Alquqa, E. K., Aldabbas, H., & Elamin, A. M. (2025). The mediating role of resilience in the relationship between attitude towards artificial intelligence and workplace well-being. Journal of Posthumanism, 5(6), 2326–2339. https://doi.org/10.63332/joph.v5i6.2341

Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Rivas-Manzano, M. d. R. d. (2024). Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Sciences, 14(7), 740. https://doi.org/10.3390/educsci14070740

Carver, C. S. (1998). Resilience and thriving: Issues, models, and linkages. Journal of Social Issues, 54(2), 245–266.

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.

Chiou, M., McCabe, F., Grigoriou, M., & Stolkin, R. (2021). Trust, shared understanding and locus of control in mixed-initiative robotic systems. In: 2021 30th IEEE International Conference on Robot & Human Interactive Communication (ROMAN) (pp. 684–691). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/RO-MAN50785.2021.9515476

Cvetkovic, A., Savolainen, I., Koike, M., & Oksanen, A. (2025). A four-wave longitudinal study on attitudes toward the use of AI in different domains—The self-determination theory and locus of control perspective. Telematics and Informatics Reports, 18, 100220. https://doi.org/10.1016/j.teler.2025.100220

Čačić, S. i Gavrilov-Jerković, V. (2013). Kognitivno-afektivni regulatorni mehanizmi kao medijatori između dimenzija afektivne vezanosti i depresije. Primenjena psihologija, 6, 358–405.

Davidović Rakić, J., Popović, E., & Minić, J. (2024). Anxiety related to artificial intelligence among educators in the Republic of Serbia. International Conference Education and Artificial Intelligence (EDAI 2024): Book of Abstracts (p. 20). Vranje: Pedagogical Faculty in Vranje, University of Niš.

Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note: How does personality matter? Relating the Five-Factor Model to technology acceptance and use. Information Systems Research, 19(1), 93–105. https://doi.org/10.1287/isre.1070.0153

Duan, H., & Zhao, W. (2024). The effects of educational artificial intelligence-powered applications on teachers’ perceived autonomy, professional development for online teaching, and digital burnout. International Review of Research in Open and Distributed Learning, 25(3), 57–76. https://doi.org/10.19173/irrodl.v25i3.7659

Fletcher, D., & Sarkar, M. (2013). Psychological resilience: A review and critique of definitions, concepts, and theory. European Psychologist, 18(1), 12–23. https://doi.org/10.1027/1016-9040/a000124

Gayed, J. M. (2025). Educators’ perspective on artificial intelligence: Equity, preparedness, and development. Cogent Education, 12(1), 2447169. https://doi.org/10.1080/2331186X.2024.2447169

Gessl, A. S., Schlögl, S., & Mevenkamp, N. (2019). On the perceptions and acceptance of artificially intelligent robotics and the psychology of the future elderly. Behaviour & Information Technology, 38(11), 1068–1087. https://doi.org/10.1080/0144929X.2019.1566499

Goldberg, L. M., Sweeney, D., Meranda, P. F., & Hughes, J. E., Jr. (1996). The Big-Five factor structure as an integrative framework: An analysis of Clarke’s AVA model. Journal of Personality Assessment, 66, 441–471.

Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1

Gu, Q., & Day, C. (2007). Teacher’s resilience: A necessary condition for effectiveness. Teaching and Teacher Education, 23(8), 1302–1316. https://doi.org/10.1016/j.tate.2006.06.006semanticscholar.org+6

Hopcan, S., Türkmen, G., & Polat, E. (2023). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 28(1), 765–782. https://doi.org/10.1007/s10639-023-12086-9

John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big-Five Inventory-Version 4a and 54. Berkeley, CA: Berkeley Institute of Personality and Social Research, University of California.

John, O. P., & Srivastava, S. (1999). The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In: L. A. Pervin & O. P. John (Eds.), Handbook of Personality: Theory and Research, 2nd ed. (pp. 102–138). New York: Guilford Press.

Judge, T. A., & Bono, J. E. (2001a). A rose by any other name: Are self-esteem, generalized self-efficacy, neuroticism, and locus of control indicators of a common construct? In: B. W. Roberts & R. Hogan (Eds.), Personality Psychology in the Workplace (pp. 93–118). Washington, DC: American Psychological Association. https://doi.org/10.1037/10434-004

Judge, T. A., & Bono, J. E. (2001b). Relationship of core self-evaluations traits—self-esteem, generalised self-efficacy, locus of control, and emotional stability—with job satisfaction and job performance: A meta-analysis. Journal of Applied Psychology, 86(1), 80–92. https://doi.org/10.1037/0021-9010.86.1.80

Kasinidou, M., Kleanthous, S., & Otterbacher, J. (2024). “Artificial intelligence is a very broad term”: How educators perceive Artificial Intelligence?. In: GoodIT ‘24: Proceedings of the 2024 International Conference on Information Technology for Social Good (pp. 315–323). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3677525.3678677

Kim, L. E., Jörg, V., & Klassen, R. M. (2019). A meta-analysis of the effects of teacher personality on teacher effectiveness and burnout. Educational Psychology Review, 31(1), 163–195. https://doi.org/10.1007/s10648-018-9458-2

Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. Work & Stress, 19(3), 192–207. https://doi.org/10.1080/02678370500297720

Larsen, R. J., & Buss, D. M. (2008). Psihologija ličnosti. Jastrebarsko: Naklada Slap.

Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on integrated fear acquisition theory. Technology in Society, 63, 101410. https://doi.org/10.1016/j.techsoc.2020.101410

Liu, Z., Li, Y., Zhu, W., He, Y., & Li, D. (2022). A meta-analysis of teachers’ job burnout and Big Five personality traits. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.822659

Mansfield, C. F., Beltman, S., Weatherby-Fell, N., & Broadley, T. (2016). Building resilience in teacher education: An evidence-informed framework. Teaching and Teacher Education, 54, 77–87. https://doi.org/10.1016/j.tate.2015.11.016

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205

Maslach, C., & Leiter, M. P. (2016). Burnout: A multidimensional perspective. In: G. Fink (Ed.), Stress: Concepts, Cognition, Emotion, and Behavior (pp. 351–357). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-12-800951-2.00044-3

Montag, C., Schulz, P. J., Zhang, H., & Li, B. J. (2025). On pessimism aversion in the context of artificial intelligence and locus of control: Insights from an international sample. AI & Soc, 40, 3349–3356. https://doi.org/10.1007/s00146-025-02186-0

Ng, T. W. H., Sorensen, K. L., & Eby, L. T. (2006). Locus of control at work: A meta-analysis. Journal of Organizational Behavior, 27(8), 1057–1087. https://doi.org/10.1002/job.416

Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology, 156(1), 68–94. https://doi.org/10.1080/00223980.2021.2012109

Pham, S. T. H., & Sampson, P. M. (2022). The development of artificial intelligence in education: A review in context. Journal of Computer Assisted Learning, 38(5), 1312–1330. https://doi.org/10.1111/jcal.12687

Pichlbauer, M. (2024). Einfluss der Persönlichkeitsvariable „Locus of Control“ auf das Vertrauen in KI-Systeme (Master’s thesis). University of Applied Sciences Upper Austria, School of Management, Steyr, Austria. https://epub.jku.at/obvulihs/content/titleinfo/10518655

Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: An initial investigation. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI ’22 Extended Abstracts, April 29–May 5, 2022, New Orleans, LA, USA) (pp. 1–7). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3491101.3519866

Polat, E. (2025). Artificial intelligence literacy, lifelong learning, and fear of innovation: Identification of profiles and relationships. Education and Information Technologies, 30, 20183–20214. https://doi.org/10.1007/s10639-025-13548-y

Polat, D. D., & İskender, M. (2018). Exploring teachers’ resilience in relation to job satisfaction, burnout, organizational commitment and perception of organizational climate. International Journal of Psychology and Educational Studies, 5(3), 1–13. https://doi.org/10.17220/ijpes.2018.03.001

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. https://doi.org/10.3758/BRM.40.3.879

Rajović, R., Davidović Rakić, J. i Erdeš Kavečan, Đ. (2021). Sagorevanje i zadovoljstvo životom prosvetnih radnika u Sloveniji u uslovima krize izazvane pandemijom Kovid 19. U: Z. Arsić (ur.), Psihologija katastrofa, vanredno stanje i njihov efekat na zdravlje (str. 331–357). Kosovska Mitrovica: Filozofski fakultet Univerziteta u Prištini sa privremenim sedištem u Kosovskoj Mitrovici.

Rehman, A. U., Mahmood, A., Bashir, S., & Iqbal, M. (2024). Technophobia as a technology inhibitor for digital learning in education: A systematic literature review. Journal of Educators Online, 21(1), 1–20. https://doi.org/10.9743/JEO.2024.21.1.1

Richards, K. A. R., Levesque-Bristol, C., Templin, T. J., & Graber, K. C. (2016). The impact of resilience on role stressors and burnout in elementary and secondary teachers. Social Psychology of Education, 19(3), 511–536. https://doi.org/10.1007/s11218-016-9346-x

Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976

Schepman, A., & Rodway, P. (2023). The General Attitudes Towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human-Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400

Schwab, R. L., Jackson, S. E., & Schuler, R. S. (1986). Educator burnout: Sources and consequences. Educational Research Quarterly, 10(3), 14–30.

Sharan, N. N., & Romano, D. M. (2020). The effects of personality and locus of control on trust in humans versus artificial intelligence. Heliyon, 6(8), e04572. https://doi.org/10.1016/j.heliyon.2020.e04572

Silagan, B. L., & Tumapon, T. (2025). Technological competence, training and support, attitude towards AI, and teachers’ acceptance. SSRN. https://doi.org/10.2139/ssrn.5250301

Skaalvik, E. M., & Skaalvik, S. (2010). Teacher self-efficacy and teacher burnout: A study of relations. Teaching and Teacher Education, 26(4), 1059–1069. https://doi.org/10.1016/j.tate.2009.11.001

Skaalvik, E. M., & Skaalvik, S. (2020). Teacher burnout: Relations between dimensions of burnout, perceived school context, job satisfaction and motivation for teaching. A longitudinal study. Teachers and Teaching, 26(7–8), 602–616. https://doi.org/10.1080/13540602.2021.1913404

Slišković, A. i Burić, I. (2018). Kratka skala otpornosti. U: A. Slišković, I. Burić, V. Ćubela Adorić, M. Nikolić i I. Tucak Junaković, (ur.), Zbirka psihologijskih skala i upitnika, Svezak 9 (str. 7–12). Zadar: Sveučilište u Zadru.

Slišković, A., Gregov, Lj. i Tokić, A. (2014). Spektorova skala radnog lokusa kontrole. U: V. Ćubela Adorić, Z. Penezić, A. Proroković i I. Tucak Junaković (ur.), Zbirka psihologijskih skala i upitnika, Svezak 7 (str. 57–64). Zadar: Sveučilište u Zadru.

Smederevac, S. i Mitrović, D. (2018). Ličnosti – metodi i modeli. Beograd: Centar za primenjenu psihologiju.

Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The brief resilience scale: Assessing the ability to bounce back. International Journal of Behavioral Medicine, 15(3), 194–200. https://doi.org/10.1080/10705500802222972

Spector, P. E. (1988). Development of the Work Locus of Control Scale. Journal of Occupational Psychology, 61(4), 335–340. https://doi.org/10.1111/j.2044-8325.1988.tb00470.x

Stănescu, D. F., & Romașcanu, M. C. (2024). The influence of AI anxiety and neuroticism on attitudes toward artificial intelligence. European Journal of Sustainable Development, 13(4), 191–202. https://doi.org/10.14207/ejsd.2024.v13n4p191

Swider, B. W., & Zimmerman, R. D. (2010). Born to burnout: A meta-analytic path model of personality, job burnout, and work outcomes. Journal of Vocational Behavior, 76(3), 487–506. https://doi.org/10.1016/j.jvb.2010.01.003

Tabachnick, B. G., & Fidell, L. S. (2021). Using Multivariate Statistics, 7th ed. Boston: Pearson.

Tugade, M., & Fredrickson, B. (2004). Resilient individuals use positive emotions to bounce back from negative emotional experiences. Journal of Personality and Social Psychology, 86, 320–333. https://doi.org/10.1037/0022-3514.86.2.320

Tusaie, K., & Dyer, J. (2004). Resilience: A historical review of the construct. Holistic Nursing Practice, 18(1), 3–10.

Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887

Weitzel, E. C., Löbner, M., Glaesmer, H., Hinz, A., Zeynalova, S., Henger, S., Engel, C., Reyes, N., Wirkner, K., Löffler, M., & Riedel-Heller, S. G. (2022). The association of resilience with mental health in a large population-based sample (LIFE-AdultStudy). International Journal of Environmental Research and Public Health, 19(23), 15944. https://doi.org/10.3390/ijerph192315944

Windle, G. (2010). What is resilience? A systematic review and concept analysis. Reviews in Clinical Gerontology, 21(2), 152–169. https://doi.org/10.1017/S0959259810000420

Yin, W., Ren, G., & Zhang, G. (2025). Mediating and moderating roles of AI literacy: How it shapes the impacts of psychological resilience on work stress and job burnout among young university teachers in China. Computers and Education: Artificial Intelligence, 6, 100451. https://doi.org/10.1016/j.caeai.2025.100451

Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7

Zhang, Y., & Xiong, P. (2025). Will the application of AI technology in higher education exacerbate teacher burnout? International Journal of High Speed Electronics and Systems. Advance online publication. https://doi.org/10.1142/S0129156425408381

Objavljeno
2025/12/28
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Оригинални научни чланак